Structured data or objects generally refers to data existing in an organized form, such as a relational database, that can be accessed and analyzed by conventional techniques (i.e. Standard Query Language, SQL). By contrast, so-called unstructured data or objects refer to objects in a textual format (i.e. faxes, e-mails, documents, voice converted to text, or a collection of these formats) that do not necessarily share a common organization. Unstructured information often remains hidden and un-leveraged by an organization primarily because it is hard to access the right information at the right time or to integrate, analyze, or compare multiple items of information as a result of their unstructured nature. There exists a need for a system and method to provide structure for unstructured information such that the unstructured objects can be accessed with powerful conventional tools (such as, for example, SQL, or other information query and/or analysis tools) and analyzed for hidden trends and patterns across a corpus of unstructured objects.
Conventional, systems and methods for accessing unstructured objects have focused on tactical searches, that seek to match keywords, an approach that has several shortcomings. For example, as illustrated in FIG. 1, a tactical search engine 110 accepts search text 100. For purposes of illustration, suppose information about insects is desired and the user-entered search text 100 is ‘bug’. The search engine scans available unstructured objects 115, including individual objects 120, 130, 140, 150, and 160. In this example, one unstructured object concerns the Volkswagen bug 120, one is about insects at night 130, one is about creepy-crawlies 140, one is about software bugs 150, and one is about garden bugs 160. The tactical search engine 110 performs keyword matching, looking for the search text 100 to appear in at least one of the unstructured objects 115. In this ‘bug’ example, only those objects about the Volkswagen bug 120, software bugs 150, and garden bugs 160 actually contain the word ‘bug’ and will be returned 170. The objects about insects at night 130, and creepycrawlies 140 may have been relevant to the search but unfortunately were not identified by the conventional tactical search engine.
One conventional method of addressing this problem allows a user to enter detailed searches utilizing phrases or Boolean logic, but successful detailed tactical searches can be extremely difficult to formulate. The user must be sophisticated enough to express their search criteria in terms of Boolean logic. Furthermore, the user needs to know precisely what he or she is searching for, in the exact language that they expect to find it. Thus, there is a need for a search mechanism to more easily locate documents or other objects of interest, preferably searching with the user's own vocabulary. Further, such mechanism should desirably enable automatically searching related words and phrases, without knowledge of advanced searching techniques.
In another conventional method, the search is done based on meaning, where each of the words or phrases typed is semantically analyzed, as if second guessing the user (for example, Use of the term Juvenile picks up teenager). This increases the result set though, making analysis of search results even more important. Also this technique is inadequate and quite inaccurate when the user is looking for a concept like “definition of terrorism” or “definition of knowledge management”, where the “concept” of the phrase is more important than the meaning of the individual words in the search term.
Even when tactical searches succeed in searching or finding information, the problem of analyzing unstructured information still remains. Analyzing unstructured information goes beyond the ability to locate information of interest. Analysis of unstructured information would allow a user to identify trends in unstructured objects as well as quickly identify the meaning of an unstructured object, without first having to read or review the entire document. Thus, there further exists a need to provide a system and methodology for analyzing unstructured information. In one situation, this need extends to system and method for tracking and optionally reporting the changing presence of words or phrases in a set of documents over time.
Prior art classification systems exist that can organize unstructured objects in a hierarchical manner. However, utilizing these classification systems to locate an object of interest requires knowing what the high-level of interest would be, and following one path of inquiry often precludes looking at other options. Thus, there is also a need for a system and method that can recognize relevant relationships between words and concepts, and can categorize an object under more than one high-level interest. Such a system and method should desirably scan objects for words or phrases and determine the presence of certain patterns that suggest the meaning, or theme, of a document, allowing for more accurate classification and retrieval.
Some prior art technologies store data and information utilizing proprietary methods and/or data structures, which prevents widespread or open access or analysis by keeping objects in a native non-standard proprietary format. Thus, there is a need to store information about unstructured objects in an open architecture and preferably in a readily accessible standard storage format, one embodiment being a relational database of which many types are known. Storage in a relational database keeps the information readily available for analysis by common tools. Where access protection is desired various known security measures may be employed as are known in the art.
The focus of many currently available prior art technologies has been to help find the one document or a known piece of information of interest from a huge source of unstructured data. However, there remains a need to analyze this untapped and largely ignored collection of unstructured data to find new trends, patterns and relationships to create actionable intelligence. This need requires the ability to look at the unstructured data in many possible ways and view points, drill down and look at details of various levels of summarizations and enable visualization of unstructured data in many ways. In short, there remains a need for a theme or concept-based method and system to analyze, categorize and query unstructured information. The present invention provides such a high precision system and method.